Can a Deep Network Understand the Land Cover Across Sensors?

Abstract

Deep learning algorithms are widely used in remote sensing image scene understanding. Generally, a large-scale annotated dataset is essential to train a deep neural network for classification. In practical terms, however, a large amount of unknown remote sensing images obtained from different sensors need to be understood which may vary from resolution, geolocation and imaging conditions compared with annotated datasets. In this paper, an unsupervised domain adaptation framework based on ResNet-18 is presented to transfer the knowledge of an existing annotated land cover dataset to other remote sensing data, decreasing the discrepancy among images across sensors. The results show a significant improvement in scene understanding of new remote sensing images.